"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division, absolute_import
import copy
import numpy as np
import random
from collections import defaultdict
from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler

AVAI_SAMPLERS = [
    'RandomIdentitySampler', 'SequentialSampler', 'RandomSampler',
    'RandomDomainSampler', 'RandomDatasetSampler'
]


class RandomIdentitySampler(Sampler):
    """Randomly samples N identities each with K instances.

    Args:
        data_source (list): contains tuples of (img_path(s), pid, camid, dsetid).
        batch_size (int): batch size.
        num_instances (int): number of instances per identity in a batch.
    """

    def __init__(self, data_source, batch_size, num_instances):
        if batch_size < num_instances:
            raise ValueError(
                'batch_size={} must be no less '
                'than num_instances={}'.format(batch_size, num_instances)
            )

        self.data_source = data_source
        self.batch_size = batch_size
        self.num_instances = num_instances
        self.num_pids_per_batch = self.batch_size // self.num_instances
        self.index_dic = defaultdict(list)
        for index, items in enumerate(data_source):
            pid = items[1]
            self.index_dic[pid].append(index)
        self.pids = list(self.index_dic.keys())
        assert len(self.pids) >= self.num_pids_per_batch

        # estimate number of examples in an epoch
        # TODO: improve precision
        self.length = 0
        for pid in self.pids:
            idxs = self.index_dic[pid]
            num = len(idxs)
            if num < self.num_instances:
                num = self.num_instances
            self.length += num - num % self.num_instances

    def __iter__(self):
        batch_idxs_dict = defaultdict(list)

        for pid in self.pids:
            idxs = copy.deepcopy(self.index_dic[pid])
            if len(idxs) < self.num_instances:
                idxs = np.random.choice(
                    idxs, size=self.num_instances, replace=True
                )
            random.shuffle(idxs)
            batch_idxs = []
            for idx in idxs:
                batch_idxs.append(idx)
                if len(batch_idxs) == self.num_instances:
                    batch_idxs_dict[pid].append(batch_idxs)
                    batch_idxs = []

        avai_pids = copy.deepcopy(self.pids)
        final_idxs = []

        while len(avai_pids) >= self.num_pids_per_batch:
            selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
            for pid in selected_pids:
                batch_idxs = batch_idxs_dict[pid].pop(0)
                final_idxs.extend(batch_idxs)
                if len(batch_idxs_dict[pid]) == 0:
                    avai_pids.remove(pid)

        return iter(final_idxs)

    def __len__(self):
        return self.length


class RandomDomainSampler(Sampler):
    """Random domain sampler.

    We consider each camera as a visual domain.

    How does the sampling work:
    1. Randomly sample N cameras (based on the "camid" label).
    2. From each camera, randomly sample K images.

    Args:
        data_source (list): contains tuples of (img_path(s), pid, camid, dsetid).
        batch_size (int): batch size.
        n_domain (int): number of cameras to sample in a batch.
    """

    def __init__(self, data_source, batch_size, n_domain):
        self.data_source = data_source

        # Keep track of image indices for each domain
        self.domain_dict = defaultdict(list)
        for i, items in enumerate(data_source):
            camid = items[2]
            self.domain_dict[camid].append(i)
        self.domains = list(self.domain_dict.keys())

        # Make sure each domain can be assigned an equal number of images
        if n_domain is None or n_domain <= 0:
            n_domain = len(self.domains)
        assert batch_size % n_domain == 0
        self.n_img_per_domain = batch_size // n_domain

        self.batch_size = batch_size
        self.n_domain = n_domain
        self.length = len(list(self.__iter__()))

    def __iter__(self):
        domain_dict = copy.deepcopy(self.domain_dict)
        final_idxs = []
        stop_sampling = False

        while not stop_sampling:
            selected_domains = random.sample(self.domains, self.n_domain)

            for domain in selected_domains:
                idxs = domain_dict[domain]
                selected_idxs = random.sample(idxs, self.n_img_per_domain)
                final_idxs.extend(selected_idxs)

                for idx in selected_idxs:
                    domain_dict[domain].remove(idx)

                remaining = len(domain_dict[domain])
                if remaining < self.n_img_per_domain:
                    stop_sampling = True

        return iter(final_idxs)

    def __len__(self):
        return self.length


class RandomDatasetSampler(Sampler):
    """Random dataset sampler.

    How does the sampling work:
    1. Randomly sample N datasets (based on the "dsetid" label).
    2. From each dataset, randomly sample K images.

    Args:
        data_source (list): contains tuples of (img_path(s), pid, camid, dsetid).
        batch_size (int): batch size.
        n_dataset (int): number of datasets to sample in a batch.
    """

    def __init__(self, data_source, batch_size, n_dataset):
        self.data_source = data_source

        # Keep track of image indices for each dataset
        self.dataset_dict = defaultdict(list)
        for i, items in enumerate(data_source):
            dsetid = items[3]
            self.dataset_dict[dsetid].append(i)
        self.datasets = list(self.dataset_dict.keys())

        # Make sure each dataset can be assigned an equal number of images
        if n_dataset is None or n_dataset <= 0:
            n_dataset = len(self.datasets)
        assert batch_size % n_dataset == 0
        self.n_img_per_dset = batch_size // n_dataset

        self.batch_size = batch_size
        self.n_dataset = n_dataset
        self.length = len(list(self.__iter__()))

    def __iter__(self):
        dataset_dict = copy.deepcopy(self.dataset_dict)
        final_idxs = []
        stop_sampling = False

        while not stop_sampling:
            selected_datasets = random.sample(self.datasets, self.n_dataset)

            for dset in selected_datasets:
                idxs = dataset_dict[dset]
                selected_idxs = random.sample(idxs, self.n_img_per_dset)
                final_idxs.extend(selected_idxs)

                for idx in selected_idxs:
                    dataset_dict[dset].remove(idx)

                remaining = len(dataset_dict[dset])
                if remaining < self.n_img_per_dset:
                    stop_sampling = True

        return iter(final_idxs)

    def __len__(self):
        return self.length


def build_train_sampler(
    data_source,
    train_sampler,
    batch_size=32,
    num_instances=4,
    num_cams=1,
    num_datasets=1,
    **kwargs
):
    """Builds a training sampler.

    Args:
        data_source (list): contains tuples of (img_path(s), pid, camid).
        train_sampler (str): sampler name (default: ``RandomSampler``).
        batch_size (int, optional): batch size. Default is 32.
        num_instances (int, optional): number of instances per identity in a
            batch (when using ``RandomIdentitySampler``). Default is 4.
        num_cams (int, optional): number of cameras to sample in a batch (when using
            ``RandomDomainSampler``). Default is 1.
        num_datasets (int, optional): number of datasets to sample in a batch (when
            using ``RandomDatasetSampler``). Default is 1.
    """
    assert train_sampler in AVAI_SAMPLERS, \
        'train_sampler must be one of {}, but got {}'.format(AVAI_SAMPLERS, train_sampler)

    if train_sampler == 'RandomIdentitySampler':
        sampler = RandomIdentitySampler(data_source, batch_size, num_instances)

    elif train_sampler == 'RandomDomainSampler':
        sampler = RandomDomainSampler(data_source, batch_size, num_cams)

    elif train_sampler == 'RandomDatasetSampler':
        sampler = RandomDatasetSampler(data_source, batch_size, num_datasets)

    elif train_sampler == 'SequentialSampler':
        sampler = SequentialSampler(data_source)

    elif train_sampler == 'RandomSampler':
        sampler = RandomSampler(data_source)

    return sampler
